Methods and apparatus to calculate video-on-demand and dynamically inserted advertisement viewing probability

ABSTRACT

Methods, apparatus, systems and articles of manufacture are disclosed to calculate video-on-demand viewing probability. Example methods disclosed herein include identifying a first set of households associated with a target set of categories that presented a first media having a first telecast delay, calculating first total tuning minutes and first total exposure minutes for the first set of households, identifying a second set of households that presented the first media having the first telecast delay and having a portion of the target set of categories, the second set of households having second total tuning minutes and second total exposure minutes for each category in the second set of households, calculating a household tuning proportion based on the first and second total tuning minutes, and calculating a household exposure proportion based on the first and second total exposure minutes, and calculating the panelist viewing probability associated with the first telecast delay based on a ratio of the household exposure proportion and the household tuning proportion.

RELATED APPLICATION

This patent arises from a continuation of U.S. patent application Ser.No. 14/618,658, filed on Feb. 10, 2015, which claims the benefit of U.S.Provisional Application Ser. No. 61/977,916, which was filed on Apr. 10,2014, U.S. Provisional Application No. 61/940,994, which was filed onFeb. 18, 2014, and U.S. Provisional Application No. 61/938,617, whichwas filed on Feb. 11, 2014, all of which are hereby incorporated hereinby reference in their entireties.

FIELD OF THE DISCLOSURE

This disclosure relates generally to market research, and, moreparticularly, to methods and apparatus to calculate video-on-demand anddynamically inserted advertisement viewing probability.

BACKGROUND

In recent years, panelist research efforts included installing meteringhardware in qualified households that fit one or more demographics ofinterest. In some cases, the metering hardware is capable of determiningwhich members of the qualified households are exposed to a particularportion of media via one or more button presses on a People Meter bycorresponding household member(s) near a media device (e.g., atelevision).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example media distribution environment in whichhouseholds may be characterized with media meter data.

FIG. 2 is a schematic illustration of an example imputation engineconstructed in accordance with the teachings of this disclosure.

FIG. 3 is a plot illustrating an example viewing index effect based onan age of collected data.

FIG. 4 is an example weighting allocation table to apply a temporalweight to collected minutes.

FIG. 5 is an example dimension subset map to illustrate independentdistribution of household dimensions used to characterize householdswith media meter data.

FIGS. 6-10 are flowcharts representative of example machine readableinstructions that may be executed to implement the example imputationengine of FIGS. 1 and 2.

FIG. 11 is an example table of viewing probability values as a functionof telecast delay.

FIG. 12 is a schematic illustration of an example processor platformthat may execute the instructions of FIGS. 6-10 to implement the exampleimputation engine of FIGS. 1 and 2.

DETAILED DESCRIPTION

Market researchers seek to understand the audience composition and sizeof media, such as radio programming, television programming and/orInternet media. Such information allows the market researchers to reportadvertising delivery and/or targeting statistics to advertisers thattarget their media (e.g., advertisements) to audiences. Additionally,such information helps to establish advertising prices commensurate withaudience exposure and demographic makeup (referred to hereincollectively as “audience configuration”). As used herein, “media”refers to any sort of content and/or advertisement which is presented orcapable of being presented by an information presentation device, suchas a television, radio, computer, smart phone or tablet. To determineaspects of audience configuration (e.g., which household member iscurrently watching a particular portion of media and the correspondingdemographics of that household member), the market researchers mayperform audience measurement by enlisting any number of consumers aspanelists. Panelists are audience members (household members) enlistedto be monitored, who divulge and/or otherwise share their media exposurehabits and demographic data to facilitate a market research study.

An audience measurement entity (e.g., The Nielsen Company (US), LLC.)typically monitors media exposure habits (e.g., viewing, listening,etc.) of the enlisted audience members via audience measurementsystem(s), such as one or more metering device(s) and a People Meter.Audience measurement typically involves determining the identity of themedia being displayed on a media presentation device (e.g., atelevision), determining demographic information of an audience, and/ordetermining which members of a household are to be associated withexposure to particular media.

Some audience measurement systems physically connect to the mediapresentation device, such as the television, to identify which channelis currently tuned by capturing a channel number, audio signaturesand/or codes identifying (directly or indirectly) the programming beingdisplayed. Physical connections between the media presentation deviceand the audience measurement system may be employed via an audio cablecoupling the output of the media presentation device to an audio inputof the audience measurement system. Additionally, audience measurementsystems prompt and/or accept audience member input to reveal whichhousehold member is currently exposed to the media presented by themedia presentation device.

As described above, audience measurement entities may employ theaudience measurement systems to include a device, such as the PeopleMeter (PM), having a set of inputs (e.g., input buttons) that are eachassigned to a corresponding member of a household. The PM is anelectronic device that is typically disposed in a media exposure (e.g.,viewing) area of a monitored household and is proximate to one or moreof the audience members. The PM captures information about the householdaudience by prompting the audience members to indicate that they arepresent in the media exposure area (e.g., a living room in which atelevision set is present) by, for example, pressing their assignedinput key on the PM. When a member of the household selects theircorresponding input, the PM identifies which household member ispresent, which includes other demographic information associated withthe household member, such as a name, a gender, an age, an incomecategory, etc. However, in the event a visitor is present in thehousehold, the PM includes at least one input (e.g., an input button)for the visitor to select. When the visitor input button is selected,the PM prompts the visitor to enter an age and a gender (e.g., viakeyboard, via an interface on the PM, etc.).

The PM may be accompanied by a meter (e.g., a set meter, anactive/passive (A/P) meter, a meter within a set top box (STB), aportable people meter (PPM), portable metering via wireless telephones,portable metering via tablets, etc.) capable of measuring mediaselections presented on the media presentation device. The meter,hereinafter referred to as a set meter, collects tuning data fromdevices, such as set-top boxes, video game devices, video cassetterecorders (VCRs), digital video recorders (DVRs) and/or digitalversatile disk (DVD) players. The meter may be associated with any typeof panel, such as a national people meter (NPM) panel, a local peoplemeter (LPM) panel, households with third party monitoring entities(e.g., Experian®), and/or any other geographic area of interest. The setmeter may monitor which station is tuned, a corresponding time-of-day inwhich the station is tuned, a duration (e.g., number of minutes) thestation is tuned, and/or to identify media associated with the tunedstation (e.g., identify a program, an advertisement, etc.). The PM andthe set meter may be separate devices and/or may be integrated into asingle unit. Audience measurement data captured by the set meter mayinclude tuning information, signatures, codes (e.g., embedded into orotherwise broadcast with broadcast media), while the PM determines anumber of and/or identification of corresponding household membersexposed to the media output by the media presentation device (e.g., thetelevision).

Data collected by the PM and/or the set meter may be stored in a memoryand transmitted via one or more networks, such as the Internet, to adata store managed by the market research entity (e.g., The NielsenCompany (US), LLC). Typically, such data is aggregated with datacollected from a large number of PMs monitoring a large number ofpanelist households. Such collected and/or aggregated data may befurther processed to determine statistics associated with householdbehavior in one or more geographic regions of interest. Householdbehavior statistics may include, but are not limited to, a number ofminutes a household media device was tuned to a particular station, anumber of minutes a household media device was used (e.g., viewed) by ahousehold panelist member and/or one or more visitors, demographics ofan audience (which may be statistically projected based on the panelistdata) and instances when the media device is on or off. While examplesdescribed herein employ the term “minutes,” such as “household tuningminutes,” “exposure minutes,” etc., any other time measurement ofinterest may be employed without limitation.

In an effort to allow collected household data to be used in a reliablemanner (e.g., a manner conforming to accepted statistical sample sizes),a relatively large number of PMs are needed. Efforts to increasestatistical validity (e.g., by increasing panel size and/or diversity)for a population of interest result in a corresponding increase in moneyspent to implement panelist households with PMs. In an effort toincrease a sample size of household behavior data, example methods,apparatus, systems and/or articles of manufacture disclosed hereinemploy the set meters to collect household panelist behavior data.Example set meters disclosed herein are distinguished from PMs in thatthe set meters of panelist households capture behavior data, but do notexplicitly identify which one of the household members is activelyexposed to media presented on the corresponding media presentationdevice (e.g., a television). In some examples, the set meter capturescodes embedded by one or more entities (e.g., final distributor audiocodes (FDAC)), and does not include one or more inputs that are to beselected by one or more household panelists to identify which panelistis currently viewing the media device. Rather than collecting audiencecomposition data directly from panelists, example methods, apparatus,systems and/or articles of manufacture disclosed herein apply one ormore models to impute which household members are exposed to particularmedia programming to collected set meter data. Such example imputationtechniques are described in further detail below and referred to hereinas “persons imputation.” In other words, examples disclosed hereinfacilitate a manner of determining a probability of household exposureactivity in a stochastic manner that takes advantage of availablepanelist data (e.g., Nielsen® panelist data, Experian® panelist data,advertising provider panelist data, etc.), and avoids the expense ofadditional PM device installation in panelist households.

Turning to FIG. 1, an example media distribution environment 100includes a network 102 (e.g., the Internet) communicatively connected topanelist households within a region of interest (e.g., a target researchgeography 104). In the illustrated example of FIG. 1, some panelisthouseholds include People Meters (PMs) and set meters (SMs) 106 and someother panelist households 108 include only SMs to capture householdmedia behavior information (e.g., particular tuned stations, time atwhich stations are tuned, identification of which media was presented(e.g., programs, advertisements, etc.)). Households having both PMs andSMs are referred to herein as PM/SM households 106. Households (HHs)that do not have a PM, but have a set meter are referred to herein as SMHHs 108. Behavior information collected by the example PM/SMs 106 andthe example SM HHs 108 are sent via the example network 102 to anexample imputation engine 110 for analysis.

As described in further detail below, the example imputation engine 110identifies and analyzes panelist data from one or more target researchgeographies, such as the example target research geography 104 ofFIG. 1. While the retrieved and/or otherwise received panelist dataincludes information that identifies demographic characteristics ofcorresponding households, only such households that also contain a PMidentify which ones of the household members are responsible forparticular media consumption. As such, the example imputation engine 110analyzes the panelist data (e.g., both PM/SM households 106 and SM HHs108) to facilitate generation of viewing probabilities of particularhousehold members in the SM HHs 108, as described in further detailbelow. Additionally, the example imputation engine 110 considers viewingbehaviors associated with recently telecast video on demand (RTVOD) andthe manner in which such behaviors affect viewing probabilities. Bydetermining a manner in which RTVOD affects viewing probabilities,advertising efforts may be implemented and/or otherwise designed bymarket researchers to target audiences in a more efficient and effectivemanner, thereby reducing wasted money and advertising campaign effortsthat target audiences disinterested and/or otherwise less likely to beconsuming the media.

The example SM HHs 108 may include panelists measured and/or otherwisemanaged by the audience measurement entity (e.g., The Nielsen Company(US), LLC.) and/or one or more other entities that have information thatidentifies a demographic composition of a corresponding household. Insome examples, marketing services companies (e.g., Experian®) collectand manage household information about participating households in amanner compliant with privacy controls to link information with SM HHsto identify a number of household members, corresponding householdmember ages, genders, education levels, etc. In still other examples,media providers, distributors and/or advertising source agencies collectand manage household information from SM HHs to identify a number ofhousehold members, corresponding household member ages, genders,education levels, etc. As described above, because SM HHs 108 do notinclude PMs, they do not include physical button inputs to be selectedby household members to identify which household member is currentlywatching particular media, and they do not include physical buttoninputs to be selected by household visitors to identify age and/orgender information. Therefore, example methods, systems, apparatusand/or articles of manufacture disclosed herein model householdcharacteristics that predict a likelihood that a particular householdmember is watching the identified media (e.g., program, advertisement,etc.) being accessed in the SM HHs 108.

Example households that include a PM will collect panelist audience datathat specifically identifies which particular household member isassociated with any particular media exposure activity. As used herein,“panelist audience data” includes both (a) media identification data(e.g., code(s) embedded in or otherwise transmitted with media,signatures, channel tuning data, etc.) and (b) person informationidentifying the corresponding household member(s) and/or visitors thatare currently watching/viewing/listening to and/or otherwise accessingthe identified media. Additionally, because the PM/SM HHs 106 alsoinclude a SM, tuning behavior data is also collected to identify aselected station, a number of minutes tuned to the selected station,and/or time-of-day information associated with the tuning behavior(s).On the other hand, SM HHs 108 include only a SM to collect media data.As used herein, “media data” and/or “media identifier information” areused interchangeably and refer to information associated with mediaidentification (e.g., codes, signatures, etc.), but does not includeperson information identifying which household member(s) and/or visitorsare currently watching/viewing/listening to and/or otherwise accessingthe identified media. As described in further detail below, examplemethods, apparatus, systems and/or articles of manufacture disclosedherein impute person identifying data to media data collected from SMHHs 108.

Although examples disclosed herein refer to collecting codes, techniquesdisclosed herein could also be applied to systems that collectsignatures and/or channel tuning data to identify media (e.g., from anattached device, from a log of a server that provides content, etc.).Audio watermarking is a technique used to identify media such astelevision broadcasts, radio broadcasts, advertisements (televisionand/or radio), downloaded media, streaming media, prepackaged media,etc. Existing audio watermarking techniques identify media by embeddingone or more audio codes (e.g., one or more watermarks), such as mediaidentifying information and/or an identifier that may be mapped to mediaidentifying information, into an audio and/or video component. In someexamples, the audio or video component is selected to have a signalcharacteristic sufficient to hide the watermark. As used herein, theterms “code” or “watermark” are used interchangeably and are defined tomean any identification information (e.g., an identifier) that may betransmitted with, inserted in, or embedded in the audio or video ofmedia (e.g., a program or advertisement) for the purpose of identifyingthe media or for another purpose such as tuning (e.g., a packetidentifying header). As used herein “media” refers to audio and/orvisual (still or moving) content and/or advertisements. To identifywatermarked media, the watermark(s) are extracted and used to access atable of reference watermarks that are mapped to media identifyinginformation.

Unlike media monitoring techniques based on codes and/or watermarksincluded with and/or embedded in the monitored media, fingerprint orsignature-based media monitoring techniques generally use one or moreinherent characteristics of the monitored media during a monitoring timeinterval to generate a substantially unique proxy for the media. Such aproxy is referred to as a signature or fingerprint, and can take anyform (e.g., a series of digital values, a waveform, etc.) representativeof any aspect(s) of the media signal(s) (e.g., the audio and/or videosignals forming the media presentation being monitored). A goodsignature is one that is repeatable when processing the same mediapresentation, but that is unique relative to other (e.g., different)presentations of other (e.g., different) media. Accordingly, the term“fingerprint” and “signature” are used interchangeably herein and aredefined herein to mean a proxy for identifying media that is generatedfrom one or more inherent characteristics of the media.

Signature-based media monitoring generally involves determining (e.g.,generating and/or collecting) signature(s) representative of a mediasignal (e.g., an audio signal and/or a video signal) output by amonitored media device and comparing the monitored signature(s) to oneor more references signatures corresponding to known (e.g., reference)media sources. Various comparison criteria, such as a cross-correlationvalue, a Hamming distance, etc., can be evaluated to determine whether amonitored signature matches a particular reference signature. When amatch between the monitored signature and one of the referencesignatures is found, the monitored media can be identified ascorresponding to the particular reference media represented by thereference signature that with matched the monitored signature. Becauseattributes, such as an identifier of the media, a presentation time, abroadcast channel, etc., are collected for the reference signature,these attributes may then be associated with the monitored media whosemonitored signature matched the reference signature. Example systems foridentifying media based on codes and/or signatures are long known andwere first disclosed in Thomas, U.S. Pat. No. 5,481,294, which is herebyincorporated by reference in its entirety.

In some examples, panelist households utilize video-on-demand (VOD)services, which permit media to be displayed within the household inresponse to a request (e.g., program selection via a menu of theset-top-box). As used herein, “video-on-demand” differs from livetelecast media based on an amount of time since the associated livetelecast media was originally available. In other words, the VODservices may allow audience members to view movies and/or programs(e.g., sitcom episodes) that have been previously broadcast/telecastlive at a prior date and time. In other examples, the VOD servicesfacilitate movie and/or program viewing for media that has not beenassociated with a prior live broadcast, but instead reflects premiumservices associated with subscription fees. In some examples, media thatis telecast to an audience based on a scheduled date and time isreferred to as “live viewing” or “linear viewing.” Examples of linearviewing include first-run telecast instances of a sitcom during aregularly scheduled day of week and corresponding time of day.

In the event the first-run telecast media is made available to anaudience at a later time, such delayed viewing is referred to asrecently telecast VOD (RTVOD), and is associated with a correspondingtelecast delay value. In some examples, the telecast delay value occursmoments after the corresponding linear viewing event, such as when anaudience member uses a digital video recorder to watch recorded media.In other examples, a media provider provides the audience member withselectable options to choose media that has been previously associatedwith linear viewing (e.g., a sitcom that was telecast 24-hours earlier).Live telecast media includes certain advertisements that are presentedto an audience during the scheduled telecast date and time (e.g., linearviewing associated with a weekly 30-minute sitcom timeslot), in whichthe advertisements are selected to target expected demographic profiles(e.g., males and females age 26-39). However, demographics of audiencesthat participate in live telecast viewing differ from demographics ofaudiences that participate in RTVOD viewing. Further still, thedemographics of audiences that use RTVOD of a first telecast delay valuemeasured from the date of live viewing may differ from the demographicsof audiences that use RTVOD of a second telecast delay value (e.g.,relatively older) measured from the date of live viewing. Knowledge ofsuch audience demographic differences allows audience measuremententities and/or advertisers to target advertisements in a manner thatmore closely aligns with the demographics to be expected by a viewingaudience. In some examples, insertion of advertisements in RTVOD mediais referred to as dynamic advertisement insertion, which can be enhancedwhen a corresponding viewing probability is known for each correspondingRTVOD telecast delay value (e.g., a probability for a demographicprofile during linear viewing as distinguished from the probability forthe same demographic profile during RTVOD occurring 3 days after acorresponding live event).

While advertisers, advertisement insertion technology entities, audiencemeasurement entities and/or, more generally, market researchers may knowthe specific demographic composition of a household (e.g., threehousehold members, one male age 33, one female age 32, one male childage 3), unless such households also include a PM, such entities will notknow which particular household members are exposed to any particularmedia displayed within the household. Additionally, in the event that anSM HH 108 requests RTVOD media a day after a corresponding live telecastof that media versus an RTVOD media request three days after thecorresponding live telecast of that media, the market researchers willnot know which household members are within the audience unless thehousehold also includes a PM. Accordingly, examples disclosed hereinidentify a viewing probability for respective household members basedon, in part, whether the media is a live telecast (linear viewing) or anRTVOD telecast of different durations (delay) from the live/originaltelecast event.

FIG. 2 is a schematic illustration of an example implementation of theimputation engine 110 of FIG. 1. In the illustrated example of FIG. 2,the imputation engine 110 includes a People Meter (PM) interface 202, aset meter (SM) interface 204, a categorizer 206, a weighting engine 210and a probability engine 212. The example probability engine 212 of FIG.2 includes an example dimension manager 214, an example cell probabilityengine 216 and an example independent distribution engine 218. Theexample cell probability engine 216 of FIG. 2 includes an examplecategory fit manager 220, an example minutes aggregator 222 and anexample probability generator 224. The example independent distributionengine 218 of FIG. 2 includes an example category qualifier 226, anexample proportion manager 228 and an example distribution engine 230.

In operation, the example PM interface 202 acquires people meter datafrom any and all PMs within the example panelist households 104. Inparticular, the example PM interface 202 acquires PM data from the PMdevices located in the example PM/SM households 106 (i.e., householdsthat have both SMs and PM devices). The PM devices have input (s) (e.g.,buttons for each household member to select to identify their respectivepresence in the audience currently exposed to media). In some examples,the PM/SM households 106 are associated with a particular geographicarea of focus, such as nationwide (sometimes referred to as a “NationalPeople Meter” (NPM)), while in other examples the PM/SM households 106are associated with a subset of a particular geographic area of focus,such as a localized geography of interest (e.g., a city within a nation(e.g., Chicago), and sometimes referred to as “Local People Meter”(LPM)). Because the acquired data from PM devices in NPMs and/or LPMsutilizes panelists and includes detailed information related tobehaviors on a persons level, models and/or probabilities may begenerated therefrom.

For example, in the event an analysis of the Charlotte designated marketarea (DMA) is desired, then the example PM interface 202 captures datafrom LPM households within a time zone corresponding to the desired DMA(e.g., the Eastern time zone). In some examples, desired data may bestreamed back to one or more storage repositories, from which theexample imputation engine 110 may retrieve the data. The example PMinterface 202 of the illustrated examples collects, acquires and/orotherwise captures PM data (panelist audience data) from panelisthouseholds 104 (having both PMs and SMs) and records or aggregates themedia exposure minutes to respective persons within the household as oneor more of the possible audience members (e.g., viewers) of thecorresponding media. In other words, the captured panelist audience datais at a persons-level rather than at a household level, whichfacilitates an ability to generate person probabilities, as described infurther detail below.

The example categorizer 206 of FIG. 2 categorizes the acquired panelistaudience data in any number of categories, such as by age, by gender, bywhether a household is of size one (e.g., a single person household) orof size two or more (e.g., two or more persons in the household), by astation/affiliate, by live media viewing, by RTVOD viewing (and/or anRTVOD age since live event), by a genre and/or by daypart. In someexamples, categories include those related to race, ethnicity,geography, language, metro vs. non-metro, etc. In still other examples,categories include an age of the head of household, a room location(e.g., a living room, a master bedroom, other bedroom, etc.), and/or thepresence of children. In the event one or more categories improveresults, it may be used for analysis, while categories that do notillustrate improvements or cause negative impacts may be removed duringthe analysis.

As used herein, categories refer to classifications associated withcollected exposure minutes (also known as “viewing minutes”). Categoriesmay include, but are not limited to, a daypart associated with collectedexposure minutes (e.g., Monday through Friday from 5:00 AM to 6:00 AM,Sunday from 10:00 PM to 1:00 AM, etc.), a station associated withcollected exposure minutes (e.g., WISN, WBBM, etc.), whether the mediais live or RTVOD, an age/gender associated with collected exposureminutes (e.g., males age 2-5, females age 35-44, etc.), and a genre(e.g., kids programs, home repair programs, music programs, sportsprograms, etc.) associated with collected exposure minutes. In stillother examples, the categorizer 206 categorizes the acquired panelistaudience data by education (e.g., 8 years or less, 9 years to highschool graduate, some college to Bachelor degree, master's degree orhigher, etc.), life stage (e.g., pre-family, young family, older family,post family, retired, etc.) and/or a number of media presentationdevices (e.g., television sets in the household. One or morecombinations of station/affiliate/genre/live vs. RTVOD/demographicattribute(s) may be categorized in different ways based on, for example,variations between data available for one or more age/gender levels. Forexample, some local markets have ten stations in which a sample size formen age 45-54 may exhibit a data sample size of statistical significancefor seven of those ten stations. In other examples, a local market mayhave relatively fewer stations where the age/gender levels are ofsufficient size to support statistical significance. In some suchexamples, the age/gender groupings are adjusted (e.g., from males age40-45 to males age 40-50) to increase an available sample size toachieve a desired statistical significance.

To impute panelist audience data (e.g., exposure minutes, which issometimes referred to herein as “viewing minutes”) to media data, theexample PM interface 202 identifies Local People Meter (LPM) data thathas been collected within a threshold period of time. On a relativescale, when dealing with, for example, television exposure, an exposureindex, which provides an indication of how well LPM data accuratelyimputes exposure minutes, may be computed in a manner consistent withEquation (1).

$\begin{matrix}{{{Exposure}\mspace{14mu}{Index}} = \frac{\begin{matrix}{{{No}.\mspace{14mu}{of}}\mspace{14mu}{imputed}\mspace{14mu}{LPM}} \\{{exposure}\mspace{14mu}{\min.\mspace{14mu}{for}}\mspace{14mu}{{ea}.\mspace{14mu}{cat}.}}\end{matrix}}{\begin{matrix}{{{No}.\mspace{14mu}{of}}\mspace{14mu}{actual}\mspace{14mu}{LPM}} \\{{exposure}\mspace{14mu}{\min.\mspace{14mu}{for}}\mspace{14mu}{{ea}.\mspace{14mu}{cat}.}}\end{matrix}}} & {{Equation}\mspace{14mu}(1)}\end{matrix}$In the illustrated example of Equation (1), the exposure index iscalculated as the ratio of the number of imputed LPM viewing minutes foreach category of interest and the number of actual LPM viewing minutesfor each category of interest.

The example exposure index of Equation (1) may be calculated on amanual, automatic, periodic, aperiodic and/or scheduled basis toempirically validate the success and/or accuracy of imputation effortsdisclosed herein. Index values closer to one (1) are indicative of agreater degree of accuracy when compared to index values that deviatefrom one (1). Depending on the type of category associated with thecollected exposure minutes, corresponding exposure index values may beaffected to a greater or lesser degree based on the age of the collecteddata. FIG. 3 is an example plot 300 of exposure index values by daypart.In the illustrated example of FIG. 3, the plot 300 includes an x-axis ofdaypart values 302 and a y-axis of corresponding exposure index values304. Index value data points labeled “1-week” appear to generally residecloser to index values of 1.00, while index value data points labeled“3-weeks” appear to generally reside further away from index values of1.00. In other words, panelist audience data that has been collectedmore recently results in index values closer to 1.00 and, thus, reflectsan imputation accuracy better than panelist audience data that has beencollected from longer than 1-week ago.

As described above, collected data that is more recent exhibits animputation accuracy that is better than an imputation accuracy that canbe achieved with relatively older collected data. Nonetheless, some datathat is relatively older will still be useful, but such older data isweighted less than data that is more recent to reflect its loweraccuracy. The example weighting engine 210 applies a temporal weight,and applies corresponding weight values by a number of days since thedate of collection. Relatively greater weight values are applied to datathat is relatively more recently collected. In some examples, weightvalues applied to collected tuning minutes and collected exposureminutes are based on a proportion of a timestamp associated therewith.For instance, a proportionally lower weight may be applied to a portionof collected minutes (e.g., tuning minutes, exposure minutes) when anassociated timestamp is relatively older than a more recently collectionportion of minutes.

FIG. 4 illustrates an example weighting allocation table 400 generatedand/or otherwise configured by the example weighting engine 210. In theillustrated example of FIG. 4, a PM/SM household 106 acquired exposureminutes (i.e., individualized panelist audience data) via a PM device(row “A”), and an SM household 108 acquired household tuning minutes(i.e., minutes tuned in a household without individualizing to aspecific person within that household) via an SM device (row “B”). Theexample individualized panelist audience and household tuning minutesare collected over a seven (7) day period. In that way, the most recentday (current day 402) is associated with a weight greater than anyindividualized panelist audience and/or household tuning minutes fromprior day(s). The example individualized panelist minutes of row “A” maybe further segmented in view of a desired category combination for agiven household. As described above, categories that characterize ahousehold may include a particular age/gender, size of household, viewedstation, live vs. RTVOD of a certain age, daypart, number oftelevisions, life stage, education level and/or other demographicattribute(s). For purposes of illustration, examples described below,the household age/gender category for the household is male, age 45-54,and the tuned station is live and associated with a premium pay channel(genre) during the daypart associated with Monday through Friday between6:00 PM and 7:00 PM.

In the illustrated example of FIG. 4, the weighting engine 210 applies aunitary weight value to the first six (6) days of individualizedpanelist minutes and household tuning minutes, and applies a weightvalue of six (6) to the most current day. While a value of six (6) isdisclosed above, like the other values used herein, such value is usedfor example purposes and is not a limitation. In operation, the exampleweighting engine 210 of FIG. 2 may employ any weighting value in whichthe most current day value is relatively greater than values for one ormore days older than the current day. The example weighting engine 210may generate a weighted sum of the collected individualized panelistaudience exposure minutes (hereinafter referred to herein as “exposureminutes”) in a manner consistent with example Equation (2), and maygenerate a weighted sum of the collected household tuning minutes in amanner consistent with example Equation (3).

$\begin{matrix}{{{Exposure}\mspace{14mu}{{Min}.}} = {\lbrack {W_{1}( {\sum\limits_{d = 1}^{n}\;{E\; M_{d}}} )} \rbrack + \lbrack {W_{2}E\; M_{c}} \rbrack}} & {{Equation}\mspace{14mu}(2)} \\{{{Tuning}\mspace{14mu}{{Min}.}} = {\lbrack {W_{1}( {\sum\limits_{d = 1}^{n}\;{T\; M_{d}}} )} \rbrack + \lbrack {W_{2}T\; M_{c}} \rbrack}} & {{Equation}\mspace{14mu}(3)}\end{matrix}$In the illustrated examples of Equation (2) and Equation (3), W₁reflects a relatively lower weighting value than W₂, in which W₂ is theweighting value associated with the current day exposure minutes value.Additionally, d reflects one of n days of the collected data prior tothe current day, EM_(d) reflects exposure minutes for corresponding daysprior to the current day, TM_(d) reflects household tuning minutes forcorresponding days prior to the current day, EM_(c) reflects exposureminutes for the current day, and TM_(c) reflects household tuningminutes for the current day.

In connection with example data shown in the illustrated example of FIG.4 (e.g., days one through six having 20, 10, 10, 0, 0 and 10 exposureminutes, respectively, the current day having 40 exposure minutes, daysone through six having 40, 30, 50, 0, 0 and 30 household tuning minutesand the current day having 50 household tuning minutes), application ofexample Equation (2) results in a weighted exposure minutes value of 290and application of example Equation (3) results in a weighted householdtuning minutes value of 450. In some examples, the probability engine212 calculates an imputation probability that an SM panelist (e.g., apanelist household with only an SM device and no associated PM device)with the aforementioned category combination of interest (e.g., male,age 45-54 tuned (live) to a premium pay channel during Monday throughFriday between the daypart of 6:00 PM and 7:00 PM) is actually viewingthis tuning session. The imputation probability is calculated by theexample probability engine 212 by dividing the weighted exposure minutes(e.g., 290 minutes) by the weighted household tuning minutes (e.g., 450minutes) to yield a 64.4% chance that the SM panelist with this samehousehold category combination is associated with this tuning behavior.While examples disclosed herein refer to probability calculations, insome examples odds may be calculated to bound results between values ofzero and one. For example, odds may be calculated as a ratio of aprobability value divided by (1-Probability). If desired, the odds maybe converted back to a probability representation.

However, while the market researcher may have a particular categorycombination of interest, a corresponding probability value accuracy maybe improved when different probability calculation techniques areapplied in view of corresponding available sample sizes of householdssharing the particular category combination of interest. As described infurther detail below, if collected LPM data associated with the categorycombination of interest (e.g., male, age 45-54, tuned (live) to premiumchannel during 6:00 PM to 7:00 PM with three household members, onetelevision and the head of household have some college credit or abachelor's degree) is greater than a threshold value, then a cellprobability technique may yield a probability value with acceptableaccuracy. As used herein, an acceptable accuracy relates to a samplesize that is capable and/or otherwise required to establish resultshaving a statistical significance. However, in the event the collectedLocal People Meter (LPM) data associated with the category combinationof interest falls below the threshold value, then the cell probabilitytechnique yields unacceptably low probability value accuracy. Instead,example methods, apparatus, systems and/or articles of manufacturedisclosed herein employ independent distribution probabilitycalculations when the collected LPM data associated with the categorycombination of interest is below a threshold value, such as below athreshold value that is capable of facilitating one or more calculationsto yield results having statistical significance.

The example category manager 214 of FIG. 2 identifies categories and/ora category combinations of interest and determines whether theparticular category combination of interest has a threshold number ofhouseholds within a donor pool. As described above, the donor pool maybe a localized geography (a Local People Meter (LPM), such as thepanelist households within the geographic region of interest 104).However, as a geographic region of interest decreases in size, acorresponding number of qualifying households that match the categorycombination of interest also decreases. In some cases, the number ofqualifying households is below a threshold value, which causes one ormore probability calculation methods (e.g., cell probability) to exhibitpoor predictive abilities and/or results that fail to yield statisticalsignificance. On the other hand, in the event the donor pool ofhouseholds exceeds a threshold value count, then such probabilitycalculation methods (e.g., cell probability) exhibit satisfactorypredictive capabilities under industry standard(s).

In operation, the example category manager 214 of FIG. 2 generates alogical “AND” condition test for a set of categories of interest. Forexample, if the categories of interest include (1) a particular stationtuned live, (2) a particular daypart, (3) a particular number ofhousehold members, (4) a particular age, (5) a particular gender, (6) aparticular number of television sets in the household, (7) a particulareducation level of the head of household, and (8) a particular lifestage, then the category manager 214 determines whether the combinationof all eight categories of interest are represented by a thresholdnumber of households within the donor pool. If so, then the examplecategory manager 214 invokes the example cell probability engine 216 tocalculate a probability value of the category combination occurringwithin SM households 108. Generally speaking, when a number ofhouseholds sharing the combination of categories of interest (e.g.,items (1) through (8) above) are greater than the threshold value, acorresponding level of confidence in probability calculation via thecell probability technique is deemed satisfactory.

In the event a market researcher seeks probability information for amale aged 50 watching a premium pay channel live (not delayed viewing)between the hours of 6:00 PM and 6:30 PM, the example category fitmanager 220 of the illustrated example identifies which previouslyestablished category groups already exist that would best fit thisdesired task. In other words, the specific and/or otherwise uniqueresearch desires of the market researcher may not align exactly withexisting categorical groups collected by LPM and/or NPM devices.Instead, the example category fit manager 220 identifies that theclosest categorical combination of industry standard and/or otherwiseexpected data is with males age 45-54 between the hours of 6:00 PM and7:00 PM. The example minutes aggregator 222 of the illustrated exampleidentifies a total number of household tuning minutes in all householdsassociated with the identified closest categorical combination, and alsoidentifies a total number of exposure minutes associated with the malesage 45-54 in such households. For example, the minutes aggregator 222may identify forty-five (45) qualifying households that have males 45-54(e.g., the household could have more than just the males 45-54) in whicha premium pay genre station was tuned live between the hours of 6:00 PMto 7:00 PM, three household members with one television set and a headof household having some college credit or bachelor's degree.

Within these forty-five (45) qualifying households, the tuning minutesaggregator 222 may identify two-hundred (200) household tuning minutestotal, but only one hundred and two (102) of those minutes wereassociated with the males 45-54. The example probability generator 224of the illustrated example calculates a probability for imputation asthe ratio of exposure minutes for the males 45-54 and the totalhousehold tuning minutes for all qualifying households in a mannerconsistent with example Equation (4).

$\begin{matrix}{{{Probability}\mspace{14mu}{of}\mspace{14mu}{Imputation}} = \frac{{Exposure}\mspace{14mu}{Minutes}\mspace{14mu}{by}\mspace{14mu}{Persons}\mspace{14mu}{of}\mspace{14mu}{Interest}}{{Tuning}\mspace{14mu}{Minutes}\mspace{14mu}{of}\mspace{14mu}{Qualifying}\mspace{14mu}{Households}}} & {{Equation}\mspace{14mu}(4)}\end{matrix}$In the illustrated example of Equation (4), the probability ofimputation using the examples disclosed above is 0.51 (i.e., 102exposure minutes divided by 200 tuning minutes, in this example). Insome examples, the probability value calculated by the example cellprobability engine 216 is retained and/or otherwise imputed to SMhouseholds 108 based on a normal distribution, such as a comparison ofthe calculated probability value to a random or pseudo-random number. Inthe event the calculated probability value is greater than the randomnumber, then the household member having the categorical combination ofinterest is credited as viewing a tuning segment. In other words, thehousehold tuning data is imputed to the SM household 108 as exposuredata for the categorical combination of interest. On the other hand, inthe event the calculated probability value is less than the random orpseudo-random number, then the household member having the categoricalcombination of interest is not credited as viewing the tuning segment.In other words, the household tuning data is not imputed to the SMhousehold 108.

As discussed above, when the combinations of all categories of interestare represented by a number of households less than a threshold valuewithin the donor pool, the cell probability calculation approach may notexhibit a level of confidence deemed suitable for statistical research.Generally speaking, a number of households in a research geography ofinterest matching a single one of the categories of interest may berelatively high. However, as additional categories of interest areadded, the number of households having an inclusive match for all suchcategories decreases. In some circumstances, the number of matchinghouseholds available in the donor pool after performing a logical “AND”of all categories of interest eventually results in a donor pool havinga population lower than a threshold value, which may not exhibitstatistical confidence when applying the cell probability techniquedescribed above. In such examples, the probability engine 212 prevents acell probability technique from being employed to calculate aprobability of whether a household of interest should be credited withexposure behavior for the categorical combination of interest (e.g.,whether the male age 45-54 of the household should be credited withcaptured exposure (tuning) behavior of the household). Instead, theexample probability engine 212 invokes the example independentdistribution engine 218 when the number of households having the desiredcombination of categories of interest is below a threshold value. Asdescribed in further detail below, instead of using a pool of householdsthat match all categories of interest, households are employed thatmatch some of the categories of interest are used when calculating aprobability of viewing.

In operation, the example category qualifier 226 of FIG. 2 identifiesall households within the donor pool (e.g., within the LPM collectiongeography, such as the Charlotte DMA) that have the same set of keypredictors (i.e., particular categories within the set of categories ofinterest). In some examples, key predictors reflect a set of categoriesthat exhibit a relatively greater degree of success than othercombinations of categories. For instance, a first set of key predictorsmay include a first set of categories related to a geography ofinterest, such as sunscreen products in geographic vicinity to oceanvacation areas, or skiing products in geographic vicinity to mountainranges. While examples disclosed herein refer to a Local People Meter(LPM), such examples are not limited thereto. In some examples, aNational People Meter (NPM) may be employed as a collection geographythat reflects a relatively larger area, such as a nation. In particular,a subset of the example eight (8) original categories of interest mayinclude (1) households matching a household size category, (2)households matching a same member gender category, and (3) householdsmatching a same member age category. In other words, while the originaleight example categories of interest included the aforementioned threecategories, the remaining categories are removed from consideration whenidentifying households from the available data pool. For example, theremaining categories are removed that are related to (4) householdsmatching a same live tuned station category, (5) households matching asame education category, (6) households matching a same number oftelevision sets category, (7) households matching a same daypartcategory, and (8) households matching a same life stage/household sizecategory.

Because, in the illustrated example, the donor pool is constructed withonly SM households 106, the example category qualifier 226 retrievesand/or otherwise obtains a total household tuning minutes value and atotal exposure minutes value for the available households meeting thesize/gender/age criteria of interest (e.g., dimensions (1), (2) and (3)from above). For example, if the size/gender/age criteria of interest isfor a household size of two or more people having a male age 45-54, thenthe example category qualifier 226 identifies a number of householdsfrom that size/gender/age subset.

FIG. 5 illustrates an example category subset map 500 created by theindependent distribution engine 226 of the example of FIG. 2. Theexample independent distribution engine assembles household tuningminutes and exposure minutes from subsets of the categories of interest.In the illustrated example of FIG. 5, the map 500 includes a totalhousehold tuning minutes count and a total exposure minutes countassociated with the key predictor categories 502 of size/age/gender. Inthis example, the category qualifier 226 identified a total oftwo-hundred (200) households matching the size/gender/age criteria.While the illustrated example of FIG. 5 includes key predictors relatedto household size, household member age, and household member gender,examples disclosed herein are not limited thereto. In some examples, amarket researcher may be seeking specific information that is alsorelated to whether specific media content was played in the household,such as a particular telecast (either live or RTVOD) sitcom, or aparticular commercial. In other words, when the particular media isselected as one of the key predictors, the market researcher may performthe analysis to determine specific viewing probabilities of specificdemographics of the audience. Continuing with the example key predictorsof FIG. 5, the two-hundred households matching the specific selection ofkey predictors (i.e., a household having 2+ members containing a male ofage 45-54) include a total of 4500 tuning minutes (i.e., minutes thatidentify a live tuned station but do not identify a correspondinghousehold member) and a total of 3600 exposure minutes (e.g., minutesfor an identified live station and also identified individuals who werepresent in the audience).

The example proportion manager 228 of FIG. 2 selects one or moreremaining categories of interest that fall outside the key predictorcategories to determine corresponding available matching households,household tuning minutes and exposure minutes. The example remainingcategories may be referred to as secondary predictors or secondarycategories that affect the probability of media exposure. While examplekey predictor categories disclosed herein include household size, genderand age, example methods, apparatus, systems and/or articles ofmanufacture may include any other, additional and/or alternate type(s)of categories for the key predictors. For instance, in the event themarket researcher seeks to determine a viewing probability associatedwith a specific media program, or a specific advertisement, thenexamples disclosed herein may assemble and/or otherwise generate keypredictor categories to include only such instances when the media ofinterest (e.g., program, advertisement, etc.) was displayed.Additionally, while example secondary categories disclosed hereininclude live tuned station, RTVOD viewing of a particular duration sincethe live telecast event (e.g., RTVOD 1-day after a corresponding livetelecast, RTVOD 3-days after the corresponding live telecast, etc.),education, number of media presentation devices (e.g., TV sets), daypartand lifestage, example methods, apparatus, systems and/or articles ofmanufacture may additionally and/or alternatively include any other typeof categories as the secondary categories.

For example, the proportion manager 228 of the illustrated exampleselects one or more secondary categories to determine a correspondingnumber of matching households, household tuning minutes and exposureminutes. Again, and as described above, the temporal units of “minutes”are employed herein as a convenience when discussing example methods,apparatus, systems and/or articles of manufacture disclosed herein, suchthat one or more additional and/or alternative temporal units (e.g.,seconds, days, hours, weeks, etc.) may be considered, withoutlimitation. In the illustrated example of FIG. 5, a live tuned stationcategory 504 (e.g., one of the secondary categories of interest) isidentified by the proportion manager 228 to have eighty (80) households,which match the desired station of interest (e.g., station “WAAA”), inwhich those households collected 1800 household tuning minutes and 1320exposure minutes. Additionally, the example proportion manager 228 ofFIG. 2 selects an education category 506 (e.g., one of the secondarycategories of interest) and determines that one-hundred and ten (110)households match the desired education level of interest (e.g.,households in which the head of household has 9 years of school to highschool graduation), in which those households collected 1755 householdtuning minutes and 1200 exposure minutes. Further, the exampleproportion manager 228 of FIG. 2 selects a number of television setscategory 508 (e.g., one of the secondary categories of interest) anddetermines that one-hundred (100) households match the desired number ofTV sets within a household value, in which those households collected2100 household tuning minutes and 1950 exposure minutes. Other examplecategories considered by the example proportion manager 228 of FIG. 2include a daypart category 510 (e.g., one of the secondary categories ofinterest), in which the proportion manager 228 of FIG. 2 determines thatone-hundred (100) households match the desired daypart category, inwhich those households collected 1365 household tuning minutes and 825exposure minutes. The example proportion manager 228 of FIG. 2 alsoselects a life stage/household size category 512 (e.g., one of thesecondary categories of interest) and determines that seventy (70)households match the desired type of life stage/household size value, inwhich those households collected 1530 household tuning minutes and 1140exposure minutes.

Generally speaking, the proportion manager 228 of the illustratedexample identifies secondary category contributions of household tuningminutes and exposure minutes independently from the household tuning andexposure minutes that may occur for only such households that match allof the desired target combination of categories of interest. After eachindividual secondary category contribution household tuning minute valueand exposure minute value is identified, the example distribution engine230 calculates a corresponding household tuning proportion and exposureproportion that is based on the key predictor household tuning andexposure minute values. As described in further detail below, theexample distribution engine 230 calculates a household tuning proportionand an exposure proportion associated with each of the secondarycategories of interest (e.g., the live tuned station category 504, theeducation category 506, the number of sets category 508, the daypartcategory 510 and the life stage/size category 512). In other words,examples disclosed herein capture, calculate and/or otherwise identifycontributory effects of one or more secondary categories of interest bycalculating and/or otherwise identifying a separate corresponding tuningproportion and separate corresponding exposure proportion for each oneof the secondary categories. As described in further detail below,separate contributory effects of the one or more secondary categoriesare aggregated to calculate expected tuning minutes and expectedexposure minutes.

In the illustrated example of FIG. 5, the distribution engine 230divides the household tuning minutes associated with the live tunedstation category 504 (e.g., 1800 household tuning minutes) by the totalhousehold tuning minutes associated with the key predictor categories502 (e.g., 4500 household tuning minutes) to calculate a correspondinglive tuned station category exposure proportion 514. Additionally, thedistribution engine 230 of the illustrated example divides the exposureminutes associated with the live tuned station category 504 (e.g., 1320exposure minutes) by the total exposure minutes associated with the keypredictor categories 502 (e.g., 3600 household viewing minutes) tocalculate a corresponding live tuned station category viewing proportion516. For the sake of example, the calculated live tuned station categorytuning proportion 514 is 0.40 (e.g., 1800 household tuning minutesdivided by 4500 total exposure minutes) and the calculated live tunedstation category viewing proportion 516 is 0.37 (e.g., 1320 exposureminutes divided by 3600 total exposure minutes).

The example distribution engine 230 of FIG. 2 also calculates ahousehold tuning proportion and exposure proportion in connection withthe example education category 506. In the illustrated example of FIG.5, the distribution engine 230 divides the household tuning minutesassociated with the education category 504 (e.g., 1755 household tuningminutes) by the total household tuning minutes associated with the keypredictor categories 502 (e.g., 4500 household tuning minutes) tocalculate a corresponding education category household tuning proportion518. Additionally, the example distribution engine 230 of theillustrated example divides the exposure minutes associated with theeducation category 506 (e.g., 1200 exposure minutes) by the totalexposure minutes associated with the key predictor categories 502 (e.g.,3600 exposure minutes) to calculate a corresponding education categoryexposure proportion 520. For the sake of example, the calculatededucation category household tuning proportion 518 is 0.39 (e.g., 1755household tuning minutes divided by 4500 total household tuning minutes)and the calculated education category exposure proportion 520 is 0.33(e.g., 1200 exposure minutes divided by 3600 total exposure minutes).

The example distribution engine 230 of FIG. 2 also calculates ahousehold tuning proportion and exposure proportion in connection withthe example household sets category 508. In the illustrated example ofFIG. 5, the distribution engine 230 divides the household tuning minutesassociated with the household sets category 508 (e.g. 2100 householdtuning minutes) by the total household tuning minutes associated withthe key predictor categories 502 (e.g., 4500 household tuning minutes)to calculate a corresponding household sets category household tuningproportion 522. Additionally, the example distribution engine 230 of theillustrated example divides the exposure minutes associated with thehousehold sets category 508 (e.g., 1950 exposure minutes) by the totalexposure minutes associated with the key predictor categories 502 (e.g.,3600 exposure minutes) to calculate a corresponding household setscategory exposure proportion 524. For the sake of example, thecalculated household sets category household tuning proportion 522 is0.47 (e.g., 2100 household tuning minutes divided by 4500 totalhousehold tuning minutes) and the calculated household sets categoryexposure proportion 524 is 0.54 (e.g., 1950 exposure minutes divided by3600 total exposure minutes).

The example distribution engine 230 of FIG. 2 also calculates ahousehold tuning proportion and exposure proportion in connection withthe example daypart category 510. In the illustrated example of FIG. 5,the distribution engine 230 divides the household tuning minutesassociated with the daypart category 510 (e.g., 1365 household tuningminutes) by the total household tuning minutes associated with the keypredictor categories 502 (e.g., 4500 household tuning minutes) tocalculate a corresponding daypart category household tuning proportion526. Additionally, the example distribution engine 230 of FIG. 2 dividesthe exposure minutes associated with the daypart category 510 (e.g., 825exposure minutes) by the total exposure minutes associated with the keypredictor categories 502 (e.g., 3600 exposure minutes) to calculate acorresponding daypart category exposure proportion 528. For the sake ofexample, the calculated daypart category household tuning proportion 526is 0.30 (e.g., 1365 household tuning minutes divided by 4500 totalhousehold tuning minutes) and the calculated daypart category exposureproportion 528 is 0.23 (e.g., 825 exposure minutes divided by 3600 totalexposure minutes).

The example distribution engine 230 of FIG. 2 also calculates ahousehold tuning proportion and exposure proportion in connection withthe example life stage/size category 512. In the illustrated example ofFIG. 5, the distribution engine 230 divides the household tuning minutesassociated with the life stage/size category 512 (e.g. 1530 householdtuning minutes) by the total household tuning minutes associated withthe key predictor categories 502 (e.g., 4500 household tuning minutes)to calculate a corresponding life stage/size category household tuningproportion 530. Additionally, the example distribution engine 230 ofFIG. 2 divides the exposure minutes associated with the life stage/sizecategory 512 (e.g., 1140 exposure minutes) by the total exposure minutesassociated with the key predictor categories 502 (e.g., 3600 exposureminutes) to calculate a corresponding life stage/size category exposureproportion 532. In this example, the calculated life stage/size categorytuning proportion 530 is 0.34 (e.g., 1530 household tuning minutesdivided by 4500 total household tuning minutes) and the calculated lifestage/size category exposure proportion 532 is 0.32 (e.g., 1140 exposureminutes divided by 3600 total exposure minutes).

As described above, each of the target combinations of categories ofinterest has an independently calculated household tuning proportionvalue and an independently calculated exposure proportion value. Theexample distribution engine 230 of FIG. 2 calculates the product of allhousehold tuning proportion values (e.g., the tuned station categoryhousehold tuning proportion 514, the education category household tuningproportion 518, the household sets category household tuning proportion522, the daypart category household tuning proportion 526, and the lifestage/size category household tuning proportion 530) to determine totalexpected household tuning minutes 534. Additionally, the exampledistribution engine 230 of FIG. 2 calculates the product of allhousehold exposure proportion values (e.g., the tuned station categoryexposure proportion 516, the education category exposure proportion 520,the household sets category exposure proportion 524, the daypartcategory exposure proportion 528, and the life stage/size categoryexposure proportion 532) to determine total expected exposure minutes536. A final independent distribution is calculated by the exampledistribution engine 230 in a manner consistent with example Equation(5), and reflects a panelist behavior probability associated with thetarget combination of categories of interest.

$\begin{matrix}{{{Independent}\mspace{14mu}{Distribution}\mspace{14mu}{Probability}} = \frac{{Expected}\mspace{14mu}{Exposure}\mspace{14mu}{Minutes}}{{Expected}\mspace{14mu}{Household}\mspace{14mu}{Tuning}\mspace{14mu}{Minutes}}} & {{Equation}\mspace{14mu}(5)}\end{matrix}$

In the example exposure and household tuning minutes discussed above,the resulting independent distribution probability is 0.52. In effect,the resulting independent distribution probability is interpreted as amale 45-54 who lives in a three (3) person household, classified as anolder family, with a head of house education of nine (9) years to highschool graduate, with two (2) television sets in the household, has a52% likelihood of watching station WAAA live during the daypart ofMonday through Friday from 9:00 AM to 12:00 PM.

While an example manner of implementing the imputation engine 110 ofFIG. 1 is illustrated in FIGS. 2-5, one or more of the elements,processes and/or devices illustrated in FIG. 2 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example people meter interface 202, the example categorizer206, the example weighting engine 210, the example media meter interface204, the example probability engine 212, the example category manager214, the example cell probability engine 216, the example category fitmanager 220, the example minutes aggregator 222, the example probabilitygenerator 224, the example independent distribution engine 218, theexample category qualifier 226, the example proportion manager 228, theexample distribution engine 230 and/or, more generally, the exampleimputation engine 110 of FIGS. 1 and 2 may be implemented by hardware,software, firmware and/or any combination of hardware, software and/orfirmware. Thus, for example, any of the example people meter interface202, the example categorizer 206, the example weighting engine 210, theexample media meter interface 204, the example probability engine 212,the example category manager 214, the example cell probability engine216, the example category fit manager 220, the example minutesaggregator 222, the example probability generator 224, the exampleindependent distribution engine 218, the example category qualifier 226,the example proportion manager 228, the example distribution engine 230and/or, more generally, the example imputation engine 110 could beimplemented by one or more analog or digital circuit(s), logic circuits,programmable processor(s), application specific integrated circuit(s)(ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)). When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example people meterinterface 202, the example categorizer 206, the example weighting engine210, the example media meter interface 204, the example probabilityengine 212, the example category manager 214, the example cellprobability engine 216, the example category fit manager 220, theexample minutes aggregator 222, the example probability generator 224,the example independent distribution engine 218, the example categoryqualifier 226, the example proportion manager 228, the exampledistribution engine 230 and/or, more generally, the example imputationengine 110 is/are hereby expressly defined to include a tangiblecomputer readable storage device or storage disk such as a memory, adigital versatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc.storing the software and/or firmware. Further still, the exampleimputation engine 110 of FIGS. 1 and 2 may include one or more elements,processes and/or devices in addition to, or instead of, thoseillustrated in FIGS. 1 and 2, and/or may include more than one of any orall of the illustrated elements, processes and devices.

Flowcharts representative of example machine readable instructions forimplementing the imputation engine 110 of FIGS. 1 and 2 are shown inFIGS. 6-10. In these examples, the machine readable instructionscomprise a program for execution by a processor such as the processor1212 shown in the example processor platform 1200 discussed below inconnection with FIG. 12. The program(s) may be embodied in softwarestored on a tangible computer readable storage medium such as a CD-ROM,a floppy disk, a hard drive, a digital versatile disk (DVD), a Blu-raydisk, or a memory associated with the processor 1212, but the entireprogram and/or parts thereof could alternatively be executed by a deviceother than the processor 1212 and/or embodied in firmware or dedicatedhardware. Further, although the example program is described withreference to the flowcharts illustrated in FIGS. 6-10, many othermethods of implementing the example imputation engine 110 mayalternatively be used. For example, the order of execution of the blocksmay be changed, and/or some of the blocks described may be changed,eliminated, or combined.

As mentioned above, the example processes of FIGS. 6-10 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a tangible computer readable storagemedium such as a hard disk drive, a flash memory, a read-only memory(ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example processes of FIGS. 6-10 may be implementedusing coded instructions (e.g., computer and/or machine readableinstructions) stored on a non-transitory computer and/or machinereadable medium such as a hard disk drive, a flash memory, a read-onlymemory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended.

The program 600 of FIG. 6 begins at block 602 where the example peoplemeter interface 202 acquires PM data associated with household membersfrom the PM devices located in the example PM/SM households 106 thathave both SM devices and PM devices. As described above, the PM deviceshave input(s) (e.g., buttons for each household member and a visitorbutton to identify their respective presence in the audience currentlyexposed to media). The example PM interface 202 identifies collecteddata that is within a threshold period of time from a current day in aneffort to weight such data according to its relative age. As describedabove in connection with example Equation (1), an accuracy of theviewing index is better when the corresponding collected data is morerecent. The example categorizer 206 categorizes the acquired PM databased on one or more categories of interest (block 604). In someexamples, the categorizer 206 categorizes and/or otherwise identifiesparticular households associated with one or more categories, such as anage/gender combination of interest, a particular household size ofinterest, a particular life stage of interest, a particular viewedstation/affiliate/genre of interest, whether media was viewed live ortime-shifted (e.g., RTVOD), whether RTVOD viewing occurred within aparticular amount of time since a corresponding live telecast event(e.g., VOD 1-day after a corresponding live telecast, VOD 3-days afterthe corresponding live telecast, VOD 7-days after the corresponding livetelecast, etc.), a particular daypart of interest, a number oftelevision sets of interest within the household (e.g., households withone television set, households with 2-3 television sets, households withthree or more television sets, etc.), and/or an education level of thehead of household. While a relatively large number of PM/SM households106 will have at least one of the aforementioned categories, asubstantially smaller number of PM/SM households 106 will represent allof the target combination of categories of interest to a marketresearching during a market study.

As described above in connection with FIG. 4, the example weightingengine 210 applies weights in proportions that are based on a number ofdays since the date of collection of the donor data (block 606). Theexample media meter interface 204 also acquires household tuning datafrom SMs in the SM households 108 (block 608). Depending on whether athreshold number of households exist in the donor pool (e.g., the donorpool of PM/SM households in the region of interest 104) that match allof the categories of interest, the example probability engine 212 willinvoke a corresponding probability calculation technique (block 610) asdescribed in further detail below in connection with FIG. 7. After oneor more probabilities have been calculated and/or otherwise generated,the example probability engine 212 invokes an RTVOD analysis (block 612)to determine one or more effects of RTVOD behavior on viewingprobability, as described in further detail below in connection withFIG. 10.

FIG. 7 includes additional detail from the illustrated example of FIG.6. When generating probabilities, the example category manageridentifies categories of interest to use. Generally speaking, examplemethods, apparatus, systems and/or articles of manufacture disclosedherein generate probabilities based on a target combination ofcategories of interest such as, for example, determining the likelihoodof viewing for (1) a male age 45-54 (2) who lives in a three-personhousehold, (3) classified as an older family (4) with the head of thehousehold having an education of nine (9) years of school to high-schoolgraduate, (5) with two television sets in the household and (6) iswatching station WAAA live (i.e., not time-shifted) (7) between thedaypart of 9:00 AM to 12:00 PM. In other examples, a market researchermay be seeking probability of viewing information for specificadvertisements or programs (e.g., sitcoms). Each advertisement and/ormedia program presented via a set-top-box (either in the PM/SM HHs 106or in the SM HHs 108) is associated with a watermark that may bedetected by the SM. Accordingly, each instance of presentation of suchmedia of interest (e.g., an advertisement and/or media program) may beaccounted for when played back in a household. Additionally, if themedia of interest is played back via a RTVOD service provided by theSTB, specific probabilities associated with particular demographiccombinations may be determined. For instance, a probability of viewing aparticular sitcom during a live telecast may have a relatively highvalue for males and females age 45-59. However, a probability of viewingthat same sitcom during RTVOD 1-day after the live telecast may have arelatively high value for males and females age 21-29, therebyreflecting alternate advertising strategies for the same media ofinterest.

The example category manager 214 identifies categories of interest forwhich a probability of viewing (exposure) is desired (block 702), suchas the example seven categories referred-to above. Based on theidentified target combination of categories of interest, such as theexample above having the male age 45-54 et al., the example categorymanager 214 determines whether the available pool of data, previouslyweighted by the example weighting engine 210, includes a thresholdnumber of households that match all (e.g., all seven) of the targetcombination of categories of interest (block 704). In some examples, thecategories of interest for which a probability is desired includesspecific media (e.g., a specific program, a specific advertisement).When the example category manager 214 identifies the categories ofinterest, a corresponding watermark may be used as search criteria whenidentifying the threshold number of households (block 704). As such, afirst iteration of the example program 600 of FIG. 6 may identify theprobability of viewing during the live telecast. However, in the event asecond iteration of the example program 600 of FIG. 6 is performed forthe same media of interest, but during an instance of RTVOD that occurs6-days after the initial telecast, a second probability of viewing mayresult that would suggest an alternate marketing strategy is needed(e.g., the first iteration was relatively high for males age 45-54, butthe second iteration was relatively low for males age 45-54, suggestingthat the males age 45-54 do not readily participate in RTVOD services).

Assuming, for the sake of example, the threshold number of households tomatch all of the categories of interest is thirty (30), and the pool ofdata includes that threshold amount of available households (block 704),the example cell probability engine 216 is invoked by the probabilityengine 212 to calculate a probability value via a cell probabilitytechnique (block 706). On the other hand, if the pool of data does notsatisfy the threshold amount of thirty households (e.g., has less than30 households) (block 704), then the example probability engine 212invokes the example independent distribution engine 218 to calculate aprobability value via an independent distribution technique (block 708).

FIG. 8 illustrates an example manner of implementing the cellprobability calculation (block 706) of FIG. 7. In the illustratedexample of FIG. 8, the category fit manager 220 culls and/or otherwiselimits tuning and viewing data to fit previously established categories(block 802). As described above, in the event a market researcher has aninterest for a male age 50, industry standard panelist data acquisitiontechniques may not exactly fit the desired demographic category.Instead, the industry standard available data may be categorized interms of males between an age range of 45-54. Because the desiredcategory of interest is for a male age 50, the example category fitmanager 220 identifies the closest relevant category grouping that willsatisfy the market researcher, which in this example, includes the groupof men between the ages of 45-54. The example minutes aggregator 222identifies a total number of household tuning minutes from the selectedcategory (block 804) and identifies a total number of exposure minutesfrom the selected category (block 806). In other words, of all thehouseholds that match the categories of men age 45-54, the total numberof household tuning minutes and exposure minutes are identified.

The example probability generator 224 of FIG. 2 calculates a probabilityfor imputation based on the aforementioned totals (block 808). Asdescribed above, the probability of imputation may be calculated by theexample probability generator 224 in a manner consistent with exampleEquation (4). The example probability generator 224 invokes a randomnumber generator to generate a random or pseudo-random number (block810) and, if the resulting random or pseudo-random number is less thanor equal to the probability value (block 812), a household member withina household having a SM 108 is assigned as a viewer of the tuningsegment (block 814). On the other hand, in the event the resultingrandom or pseudo-random number is not less than or equal to theprobability value, then the household member within the household havingthe SM 108 is not assigned as a viewer of the tuning segment (block816).

Returning to block 704 of FIG. 7, and continuing with the assumptionthat the threshold number of households to match all of the categoriesof interest is thirty (30), and the pool of data fails to include thatthreshold number of qualifying households (block 704), then the exampleindependent distribution engine 218 is invoked by the probability engine212 to calculate a probability value via an independent distributiontechnique (block 710).

FIG. 9 illustrates an example implementation of the independentdistribution probability calculation (block 708) of FIG. 7. In theillustrated example of FIG. 9, the category qualifier 226 identifies allpanelist households (e.g., LPM, NPM, etc.) within the donor pool thathave the same set of key predictors (block 902). Additionally, theexample category qualifier 226 identifies a corresponding number oftotal tuning minutes associated with the key predictors, and acorresponding number of total household exposure minutes associated withthe key predictors. As described above, key predictors may refer to aparticular combination of a household size, a gender of interest withinthe household, and/or an age of interest within the household. Forexample, the category qualifier 226 may identify all households withinthe donor pool that have two or more household members, in which one ofthem is a male age 45-54. For illustration purposes, assume the examplecategory qualifier identified two-hundred (200) households that have twoor more members therein, in which one of them is a male age 45-54. Alsoassume that the combined number of identified households (200) reflect4500 total household tuning minutes and 3600 total exposure minutes.

In addition to key predictors having an influence on the probability ofviewing, one or more additional secondary predictors may also influencethe probability of viewing. As described above, the market researchermay have a combined set or target combination of categories of interest,but a number of households having all of those combined set ofcategories of interest does not exceed a threshold value (e.g., thirty(30) households). However, while the combined set of categories ofinterest may not be represented en masse from the donor pool, subportions of the combined set or target combination of categories mayinclude a relatively large representation within the donor pool. Examplemethods, apparatus, systems and/or articles of manufacture disclosedherein identify independent sub portions (subgroups) of the combined setof categories of interest and corresponding households associated witheach subgroup of interest, which are applied independently to calculatea household exposure probability.

The example proportion manager 228 identifies a number of householdsfrom the key predictors group (e.g., 200 households having a size 2+ anda male age 45-54) that match a subgroup of interest (block 904). Fromthe subgroup of interest, the example proportion manager 228 identifiesa number of household tuning minutes and divides that value by the totalhousehold tuning minutes to calculate a household tuning proportionassociated with the subgroup of interest (block 906). For example, ifthe subgroup of interest is all households tuned to the same livestation (e.g., WAAA) (e.g., the live tuned station category) and suchhouseholds reflect 1800 tuning minutes, then the example proportionmanager 228 divides 1800 by the total household tuning minutes of 4500to calculate a tuned station category household tuning proportion of0.40 (block 906). The example proportion manager 228 also identifies anumber of exposure minutes and divides that value by the total exposureminutes to calculate an exposure proportion associated with the subgroupof interest (e.g., the example tuned station category) (block 908). Forexample, if the subgroup of interest is all households tuned to the samelive station (e.g., WAAA) (e.g., the household live tuned stationdimension) and such households reflect 1320 exposure minutes, then theexample proportion manager 228 divides 1320 by the total exposureminutes of 3600 to calculate a tuned station category exposureproportion of 0.37 (block 908). If more subgroups of interest from thedonor pool are available (block 910), then the example proportionmanager 228 selects the next subgroup of interest (block 912) andcontrol returns to block 904.

After category household tuning proportion values and exposureproportion values have been calculated for each subgroup of interest,the example distribution engine 230 calculates the product of allhousehold tuning proportion values and the total household tuningminutes (e.g., 4500 in this example) from the categories of interest(block 914), and calculates the product of all exposure proportionvalues and the total exposure minutes (e.g., 3600 in this example) fromthe categories of interest (block 916). A final independent distributionprobability may then be calculated as the ratio of the exposure minutesand the household tuning minutes in a manner consistent with exampleEquation (5) (block 918). For example, and as described above inconnection with FIG. 5, the resulting ratio of expected exposure minutes(17.47) and expected household tuning minutes (33.65) may be a value of0.52. This resulting ratio indicates a 52% likelihood that the panelistmember is a male age 45-54 that lives in a three person household,classified as an older family, with the head of household education of 9years to high school graduate, with two television sets in thehousehold, and watching station WAAA live (i.e., not time-shifted) onMondays through Fridays between 9:00 AM to 12:00 PM.

FIG. 10 illustrates an example implementation of the RTVOD analysis(block 612) of FIG. 6. In the illustrated example of FIG. 10, theimputation engine 110 identifies media of interest for which a marketresearcher may desire viewing probability information (block 1002). Asdescribed above, media of interest may include a sitcom, a movie, anadvertisement, etc. In some examples, the media of interest includes acorresponding code and/or is associated with a particular signature(e.g., an audio signature) to allow identification of the media ofinterest. Generally speaking, the media of interest may have aparticular viewing probability depending on, in part, a particulardemographic audience configuration and whether the media of interest wasviewed live (linear viewing) during a regularly scheduled date/time, orwhether the media of interest was viewed after a particular delay.

The example category manager 214 identifies demographic categories ofinterest (block 1004) for which viewing probability information isdesired. While the illustrated example of FIG. 10 identifies oneparticular combination of demographic categories of interest so that aviewing probability effect can be identified as a function of RTVODtelecast delay, examples disclosed herein are not limited thereto. Forexample, the RTVOD analysis 612 of FIG. 10 may be iterated to identifycorresponding trends of viewing probability for any number of differentdemographic combinations of interest. The example probability engine 212identifies a linear viewing probability associated with the media ofinterest and the demographic categories of interest (block 1006) toserve as a baseline when analyzing trends that may be associated withRTVOD telecast delay analysis.

In the event additional RTVOD activity is available (block 1008) (e.g.,after determining cell probabilities (block 706 of FIGS. 7 and 8) and/orafter determining independent distribution probabilities (block 708 ofFIGS. 7 and 9)), then the example probability engine 212 identifiesavailable viewing probabilities in a subsequent temporal sequence fromthe linear viewing probability (block 1010). For example, theprobability engine 212 identifies an available viewing probabilityassociated with an instance of RTVOD telecast delay of one day. Theexample program 612 returns to block 1008, where the example probabilityengine 212 identifies whether additional RTVOD telecast delay valueshave occurred, such as an instance of RTVOD telecast delay of two days.If so, the example probability engine 212 identifies a correspondingviewing probability associated with the telecast delay of two days.

In the event that all values and/or variations of RTVOD telecast delayassociated with the selected media of interest and the selecteddemographic categories of interest have been considered (block 1008),then the example imputation engine 110 generates viewing probabilitytrend information (block 1012).

FIG. 11 illustrates an example table 1100 generated by the imputationengine 110 having viewing probability values as a function of differingtelecast delay values. In the illustrated example of FIG. 11, the table1100 identifies that the viewing probability information is associatedwith corresponding media of interest 1102. For the sake of example, themedia of interest 1102 is labeled as “Media 123,” which could beindicative of a particular sitcom, a particular movie, a particularadvertisement, etc. Additionally, the example table 1100 identifiescorresponding demographics of interest 1104, which is shown in theillustrated example of FIG. 11 as “Male age 45-54 in a Household of size2+.”

The example table 1100 of FIG. 11 includes an RTVOD stage column 1106and a viewing probability column 1108. Example RTVOD stage values areindicative of different telecast delay values that may range fromimmediate (e.g., live or non-time-shifted viewing) 1110, to any numberof seconds, minutes, hours, days or weeks beyond an initial linearviewing event (e.g., a date/time when the media of interest wasinitially available to audiences for viewing). As shown in theillustrated example of FIG. 11, the highest relative viewing probabilityof “Media 123” for males age 45-54 living in households of size 2+ is52%, which occurs during live viewing. However, the example probabilityengine 212 identifies a decreasing trend of viewing probabilities as thetelecast delay value increases. Such trending information may reveal oneor more marketing opportunities and/or advertising campaign adjustmentopportunities for market researchers.

FIG. 12 is a block diagram of an example processor platform 1200 capableof executing the instructions of FIGS. 6-10 to implement the imputationengine 110 of FIGS. 1 and 2. The processor platform 1200 can be, forexample, a server, a personal computer, an Internet appliance, a digitalvideo recorder, a personal video recorder, a set top box, or any othertype of computing device.

The processor platform 1200 of the illustrated example includes aprocessor 1212. The processor 1212 of the illustrated example ishardware. For example, the processor 1212 can be implemented by one ormore integrated circuits, logic circuits, microprocessors or controllersfrom any desired family or manufacturer.

The processor 1212 of the illustrated example includes a local memory1213 (e.g., a cache). The processor 1212 of the illustrated example isin communication with a main memory including a volatile memory 1214 anda non-volatile memory 1216 via a bus 1218. The volatile memory 1214 maybe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory(RDRAM) and/or any other type of random access memory device. Thenon-volatile memory 1216 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 1214,1216 is controlled by a memory controller.

The processor platform 1200 of the illustrated example also includes aninterface circuit 1220. The interface circuit 1220 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 1222 are connectedto the interface circuit 1220. The input device(s) 1222 permit(s) a userto enter data and commands into the processor 1212. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 1224 are also connected to the interfacecircuit 1220 of the illustrated example. The output devices 1224 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a printer and/or speakers). The interface circuit 1220 ofthe illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip or a graphics driver processor.

The interface circuit 1220 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network1226 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 1200 of the illustrated example also includes oneor more mass storage devices 1228 for storing software and/or data.Examples of such mass storage devices 1228 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 1232 of FIGS. 6-10 may be stored in the massstorage device 1228, in the volatile memory 1214, in the non-volatilememory 1216, and/or on a removable tangible computer readable storagemedium such as a CD or DVD.

From the foregoing, it will appreciated that the above disclosedmethods, apparatus and articles of manufacture allow audiencemeasurement techniques to occur with a substantially larger quantity ofhouseholds, by employing set meter devices instead of relatively moreexpensive people meter devices. Examples disclosed herein permit adetermination of behavior probability that can be applied to householdsthat do not have a People Meter device and, instead, employ the SM thatcaptures codes, signatures and/or tuning behavior data. Such examplesallow behavior probability calculations based on utilization of otherhouseholds that include the People Meter device, in which thecalculations reveal behavior probabilities in a stochastic manner thatadheres to expectations of statistical significance. Further, byidentifying viewing probabilities based on a type of RTVOD viewingbehavior, advertising waste may be reduced (e.g., minimized) so thatmarketing may be targeted to demographic characteristics related toaudiences that are most likely to be consuming media.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. A computer-implemented method to reduce waste ofa first advertising targeting strategy for a first media, the methodcomprising: identifying, by executing an instruction with a processor, afirst set of households associated with a target set of categoriesassociated with the first advertising targeting strategy, the firstmedia presented in the first set of households with a first time shiftdelay; calculating, by executing an instruction with the processor, afirst viewing probability associated with the first time shift delaybased on a ratio of (a) exposure minutes of the first media in the firstset of households, and (b) tuning minutes of the first media in thefirst set of households; identifying, by executing an instruction withthe processor, a second set of households associated with the target setof categories associated with the first advertising targeting strategy,the first media presented in the second set of households with a secondtime shift delay; and reducing, by executing an instruction with theprocessor, waste of the first advertising targeting strategy of thefirst media associated with the second time shift delay by selecting asecond advertising targeting strategy for the first media in response todetermining that the second time shift delay of the first media has asecond viewing probability that is less than the first viewingprobability, the determination being based on a ratio of (a) exposureminutes of the first media in the second set of households, and (b)tuning minutes of the first media in the second set of households. 2.The method as defined in claim 1, further including generating a list ofviewing probabilities of the first media associated with subsequent timeshift delay values from the first time shift delay.
 3. The method asdefined in claim 2, further including identifying viewing probabilitytrend information for the first advertising strategy based on the listof viewing probabilities.
 4. The method as defined in claim 1, whereinthe first time shift delay is associated with live viewing.
 5. Themethod as defined in claim 1, wherein the first advertising targetingstrategy includes targeting a first demographic of interest.
 6. Themethod as defined in claim 1, wherein the second advertising targetingstrategy includes a second demographic of interest.
 7. The method asdefined in claim 6, wherein the second demographic of interest isdifferent from the first demographic of interest and includes analternate viewing probability that is greater than the second viewingprobability.
 8. The method as defined in claim 1, wherein the first timeshift delay includes a telecast delay.
 9. An apparatus to reduce wasteof a first advertising targeting strategy for a first media, theapparatus comprising: a category manager to: identify a first set ofhouseholds associated with a target set of categories associated withthe first advertising targeting strategy, the first media presented inthe first set of households with a first time shift delay; and identifya second set of households associated with the target set of categoriesassociated with the first advertising targeting strategy, the firstmedia presented in the second set of households with a second time shiftdelay; a probability engine to: calculate a first viewing probabilityassociated with the first time shift delay based on a ratio of (a)exposure minutes of the first media in the first set of households, and(b) tuning minutes of the first media in the first set of households;and reduce waste of the first advertising targeting strategy of thefirst media associated with the second time shift delay by selecting asecond advertising targeting strategy for the first media in response todetermining that the second time shift delay of the first media has asecond viewing probability that is less than the first viewingprobability, the determination being based on a ratio of (a) exposureminutes of the first media in the second set of households, and (b)tuning minutes of the first media in the second set of households. 10.The apparatus as defined in claim 9, wherein the category manager is togenerate a list of viewing probabilities of the first media associatedwith subsequent time shift delay values from the first time shift delay.11. The apparatus as defined in claim 10, wherein the category manageris to identify viewing probability trend information for the firstadvertising strategy based on the list of viewing probabilities.
 12. Theapparatus as defined in claim 9, wherein the first time shift delay isassociated with live viewing.
 13. The apparatus as defined in claim 9,wherein the probability engine is to target a first demographic ofinterest with the first advertising targeting strategy.
 14. Theapparatus as defined in claim 9, wherein the probability engine is totarget a second demographic of interest with the second advertisingtargeting strategy.
 15. A tangible computer-readable storage mediumcomprising instructions that, when executed, cause a processor to, atleast: identify a first set of households associated with a target setof categories associated with a first advertising targeting strategy, afirst media presented in the first set of households with a first timeshift delay; calculate a first viewing probability associated with thefirst time shift delay based on a ratio of (a) exposure minutes of thefirst media in the first set of households, and (b) tuning minutes ofthe first media in the first set of households; identify a second set ofhouseholds associated with the target set of categories associated withthe first advertising targeting strategy, the first media presented inthe second set of households with a second time shift delay; and reducewaste of the first advertising targeting strategy of the first mediaassociated with the second time shift delay by selecting a secondadvertising targeting strategy for the first media in response todetermining that the second time shift delay of the first media has asecond viewing probability that is less than the first viewingprobability, the determination being based on a ratio of (a) exposureminutes of the first media in the second set of households, and (b)tuning minutes of the first media in the second set of households. 16.The computer-readable instructions as defined in claim 15 that, whenexecuted, cause the processor to generate a list of viewingprobabilities of the first media associated with subsequent time shiftdelay values from the first time shift delay.
 17. The computer-readableinstructions as defined in claim 16 that, when executed, cause theprocessor to identify viewing probability trend information for thefirst advertising strategy based on the list of viewing probabilities.18. The computer-readable instructions as defined in claim 15 that, whenexecuted, cause the processor to associate the first time shift delaywith live viewing activity.
 19. The computer-readable instructions asdefined in claim 15 that, when executed, cause the processor to target asecond demographic of interest during the second advertising targetingstrategy.
 20. The computer-readable instructions as defined in claim 19that, when executed, cause the processor to identify that the seconddemographic of interest is different from the first demographic ofinterest and includes an alternate viewing probability that is greaterthan the second viewing probability.